iSAM: Incremental Smoothing and Mapping

What is iSAM?

iSAM is an optimization library for sparse nonlinear problems
as encountered in simultaneous localization and mapping
(SLAM). The iSAM library provides efficient algorithms for batch
and incremental optimization, recovering the exact least-squares
solution. The library can easily be extended to new problems,
and functionality for often encountered 2D and 3D SLAM problems
is already provided. The iSAM algorithm was originally developed
by Michael Kaess
and Frank
Dellaert at Georgia
Tech.

Parts of the Manhattan world dataset during incremental optimization.

Why Use iSAM?

iSAM provides a range of advantages over other
state-of-the-art SLAM algorithms, for details please see:

Download

iSAM runs on both Linux and Mac OS X (Windows is not
supported). iSAM requires cmake, and depends on SuiteSparse and
the SDL library for visualization. Detailed installation
instructions are part of the included documentation ("make
doc").>

Many thanks to Richard Roberts for his help with this
software. Thanks also to John McDonald, Ayoung Kim, Ryan
Eustice, Aisha Walcott, Been Kim and Abe Bachrach for their
feedback and patience with earlier versions.